Abstract Conditional Logistic GLMM Inference

Fits one likelihood with a conditional-logistic contribution from discordant matched pairs and a random-intercept logistic GLMM contribution from concordant matched pairs and reservoir subjects.

Super class

Inference -> InferenceAbstractKKCondLogitGLMM

Methods

Public methods

• InferenceAbstractKKCondLogitGLMM$new()

• InferenceAbstractKKCondLogitGLMM$compute_estimate()

• InferenceAbstractKKCondLogitGLMM$compute_estimate_with_bootstrap_weights()

• InferenceAbstractKKCondLogitGLMM$compute_asymp_confidence_interval()

• InferenceAbstractKKCondLogitGLMM$compute_asymp_two_sided_pval()

• InferenceAbstractKKCondLogitGLMM$clone()

+ inherited public methods from Inference

• Inference$capabilities()
• Inference$compute_exact_confidence_interval()
• Inference$compute_exact_two_sided_pval_for_treatment_effect()
• Inference$duplicate()
• Inference$get_analysis_data()
• Inference$get_covariates()
• Inference$get_design_object()
• Inference$get_model_formula()
• Inference$get_nonestimable_reason()
• Inference$get_nonestimable_stage()
• Inference$get_optimization_alg()
• Inference$get_response()
• Inference$get_response_type()
• Inference$get_treatment()
• Inference$is_nonestimable()
• Inference$set_optimization_alg()
• Inference$set_seed()
• Inference$supports()

---

InferenceAbstractKKCondLogitGLMM$new()

Initialize KK conditional-logit GLMM incidence inference, validate the binary response, and prepare the matched-pair conditional likelihood and reservoir mixed-model components. See InferenceAbstractKKCondLogitGLMM.

Usage

InferenceAbstractKKCondLogitGLMM$new(
  des_obj,
  model_formula = NULL,
  max_abs_reasonable_coef = 50,
  max_abs_reasonable_se = 10,
  max_abs_log_sigma = 8,
  verbose = FALSE,
  smart_cold_start_default = NULL,
  optimization_alg = NULL
)

Arguments

des_obj

A completed Design object with an incidence or proportion response.

model_formula

Optional formula for covariate adjustment.

max_abs_reasonable_coef

Cap for reasonable coefficient estimates.

max_abs_reasonable_se

Cap for reasonable treatment standard errors.

max_abs_log_sigma

Cap for reasonable log random effect variance.

verbose

Logical. Whether to print progress messages.

smart_cold_start_default

Logical. Whether to use smart starting values for the optimizer.

optimization_alg

Character. Optimization algorithm (default "lbfgs").

---

InferenceAbstractKKCondLogitGLMM$compute_estimate()

Computes the class-specific treatment-effect estimate; see Inference.

Usage

InferenceAbstractKKCondLogitGLMM$compute_estimate(estimate_only = FALSE)

Arguments

estimate_only

Logical. If TRUE, skip variance component calculations.

---

InferenceAbstractKKCondLogitGLMM$compute_estimate_with_bootstrap_weights()

Recomputes the class-specific treatment estimate for a bootstrap sample; see InferenceNonParamBootstrap.

Usage

InferenceAbstractKKCondLogitGLMM$compute_estimate_with_bootstrap_weights(
  subject_or_block_weights,
  estimate_only = FALSE
)

Arguments

subject_or_block_weights

Numeric vector. Row weights for bootstrap.

estimate_only

Logical. If TRUE, skip variance component calculations.

---

InferenceAbstractKKCondLogitGLMM$compute_asymp_confidence_interval()

Uses the shared asymptotic confidence-interval contract; see InferenceAsymp.

Usage

InferenceAbstractKKCondLogitGLMM$compute_asymp_confidence_interval(
  alpha = 0.05
)

Arguments

alpha

Numeric. Significance level (default 0.05).

---

InferenceAbstractKKCondLogitGLMM$compute_asymp_two_sided_pval()

Uses the shared asymptotic two-sided p-value contract; see InferenceAsymp.

Usage

InferenceAbstractKKCondLogitGLMM$compute_asymp_two_sided_pval(delta = 0)

Arguments

delta

Numeric. Null treatment effect value (default 0).

---

InferenceAbstractKKCondLogitGLMM$clone()

The objects of this class are cloneable with this method.

Usage

InferenceAbstractKKCondLogitGLMM$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.